Level set extraction and volume rendering of anatomical brain tissue from medical volumetric images

Abstract

This paper proposes an approach integrating level set method with ray casting for image segmentation and visualization. A prior probability derived from Kullback--Leibler information number to estimate the targets (e.g., tumor and tissue) is firstly incorporated into Bayesian level set method. The Bayesian level set method is then used to continuously segment the targets from a series of brain images. To facilitate 3D volume visualization of medical-image dataset, ray casting and marching cubes algorithm are conducted to render the targets and construct the surface of the targets. Experiment results are finally reported in terms of segmenting, rendering, and surface reconstructing of the tumor, tissue, and whole brain.